Search Results for author: Tom Lenaerts

Found 6 papers, 2 papers with code

Dealing with Expert Bias in Collective Decision-Making

1 code implementation25 Jun 2021 Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé

Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives.

Decision Making

Voluntary safety commitments provide an escape from over-regulation in AI development

no code implementations8 Apr 2021 The Anh Han, Tom Lenaerts, Francisco C. Santos, Luis Moniz Pereira

With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as the hidden biases in their creation have led to a demand for regulation to address such issues.

Artificial Intelligence Development Races in Heterogeneous Settings

no code implementations30 Dec 2020 Theodor Cimpeanu, Francisco C. Santos, Luis Moniz Pereira, Tom Lenaerts, The Anh Han

Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues.

Mediating Artificial Intelligence Developments through Negative and Positive Incentives

no code implementations1 Oct 2020 The Anh Han, Luis Moniz Pereira, Tom Lenaerts, Francisco C. Santos

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy.

To regulate or not: a social dynamics analysis of the race for AI supremacy

no code implementations26 Jul 2019 The Anh Han, Luis Moniz Pereira, Francisco C. Santos, Tom Lenaerts

As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements.

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

3 code implementations20 Sep 2018 Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher

In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required.


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